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Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months

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  • José M. Garrido

    (Department of Surgery and Surgical Specialties, University of Granada, 18016 Granada, Spain
    Biosanitary Research Institute of Granada (ibs.GRANADA), 18016 Granada, Spain
    Institute of Biopathology and Regenerative Medicine (IBIMER), University of Granada, 18016 Granada, Spain)

  • David Martínez-Rodríguez

    (Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Fernando Rodríguez-Serrano

    (Biosanitary Research Institute of Granada (ibs.GRANADA), 18016 Granada, Spain
    Institute of Biopathology and Regenerative Medicine (IBIMER), University of Granada, 18016 Granada, Spain)

  • Sorina-M. Sferle

    (Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain)

  • Rafael-J. Villanueva

    (Instituto Universitario de Matemática Multidisciplinar, Universitat Politècnica de València, 46022 Valencia, Spain)

Abstract

Mathematical models have been remarkable tools for knowing in advance the appropriate time to enforce population restrictions and distribute hospital resources. Here, we present a mathematical Susceptible-Exposed-Infectious-Recovered (SEIR) model to study the transmission dynamics of COVID-19 in Granada, Spain, taking into account the uncertainty of the phenomenon. In the model, the patients moving throughout the hospital’s departments (intra-hospitalary circuit) are considered in order to help to optimize the use of a hospital’s resources in the future. Two main seasons, September–April (autumn-winter) and May–August (summer), where the hospital pressure is significantly different, have been included. The model is calibrated and validated with data obtained from the hospitals in Granada. Possible future scenarios have been simulated. The model is able to capture the history of the pandemic in Granada. It provides predictions about the intra-hospitalary COVID-19 circuit over time and shows that the number of infected is expected to decline continuously from May without an increase next autumn–winter if population measures continue to be satisfied. The model strongly suggests that the number of infected cases will reduce rapidly with aggressive vaccination policies. The proposed study is being used in Granada to design public health policies and perform wise re-distribution of hospital resources in advance.

Suggested Citation

  • José M. Garrido & David Martínez-Rodríguez & Fernando Rodríguez-Serrano & Sorina-M. Sferle & Rafael-J. Villanueva, 2021. "Modeling COVID-19 with Uncertainty in Granada, Spain. Intra-Hospitalary Circuit and Expectations over the Next Months," Mathematics, MDPI, vol. 9(10), pages 1-21, May.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:10:p:1132-:d:555897
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    References listed on IDEAS

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    1. Avila-Ponce de León, Ugo & Pérez, Ángel G.C. & Avila-Vales, Eric, 2020. "An SEIARD epidemic model for COVID-19 in Mexico: Mathematical analysis and state-level forecast," Chaos, Solitons & Fractals, Elsevier, vol. 140(C).
    2. Sarkar, Kankan & Khajanchi, Subhas & Nieto, Juan J., 2020. "Modeling and forecasting the COVID-19 pandemic in India," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    3. Nicole C. J. Brienen & Aura Timen & Jacco Wallinga & Jim E. Van Steenbergen & Peter F. M. Teunis, 2010. "The Effect of Mask Use on the Spread of Influenza During a Pandemic," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1210-1218, August.
    4. Barmparis, G.D. & Tsironis, G.P., 2020. "Estimating the infection horizon of COVID-19 in eight countries with a data-driven approach," Chaos, Solitons & Fractals, Elsevier, vol. 135(C).
    5. Smriti Mallapaty, 2021. "Are COVID vaccination programmes working? Scientists seek first clues," Nature, Nature, vol. 589(7843), pages 504-505, January.
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